Martin Ritzert
Zitiert von
Zitiert von
Weisfeiler and leman go neural: Higher-order graph neural networks
C Morris, M Ritzert, M Fey, WL Hamilton, JE Lenssen, G Rattan, M Grohe
Proceedings of the AAAI Conference on Artificial Intelligence 33, 4602-4609, 2019
Learning first-order definable concepts over structures of small degree
M Grohe, M Ritzert
2017 32nd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), 1-12, 2017
Learning MSO-definable hypotheses on strings
M Grohe, C Löding, M Ritzert
International Conference on Algorithmic Learning Theory, 434-451, 2017
Graph neural networks for maximum constraint satisfaction
J Toenshoff, M Ritzert, H Wolf, M Grohe
Frontiers in artificial intelligence 3, 98, 2021
Graph Learning with 1D Convolutions on Random Walks
J Toenshoff, M Ritzert, H Wolf, M Grohe
arXiv preprint arXiv:2102.08786, 2021
The effects of randomness on the stability of node embeddings
T Schumacher, H Wolf, M Ritzert, F Lemmerich, M Grohe, M Strohmaier
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021
RUN-CSP: Unsupervised Learning of Message Passing Networks for Binary Constraint Satisfaction Problems
J Toenshoff, M Ritzert, H Wolf, M Grohe
arXiv preprint arXiv:1909.08387, 2019
Learning Definable Hypotheses on Trees
E Grienenberger, M Ritzert
22nd International Conference on Database Theory (ICDT 2019), 2019
On the Parameterized Complexity of Learning Logic
S van Bergerem, M Grohe, M Ritzert
CoRR abs/2102.12201, 2021
Graph Machine Learning for Design of High-Octane Fuels
JG Rittig, M Ritzert, AM Schweidtmann, S Winkler, JM Weber, P Morsch, ...
arXiv preprint arXiv:2206.00619, 2022
On the Parameterized Complexity of Learning First-Order Logic
S van Bergerem, M Grohe, M Ritzert
Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2022
Optimal Weak to Strong Learning
K Green Larsen, M Ritzert
arXiv e-prints, arXiv: 2206.01563, 2022
Learning on graphs with logic and neural networks
M Ritzert
Dissertation, RWTH Aachen University, 2021, 2021
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